Advanced Lane Finding Project

The goals / steps of this project are the following:

  • Compute the camera calibration matrix and distortion coefficients given a set of chessboard images.
  • Apply a distortion correction to raw images.
  • Use color transforms, gradients, etc., to create a thresholded binary image.
  • Apply a perspective transform to rectify binary image ("birds-eye view").
  • Detect lane pixels and fit to find the lane boundary.
  • Determine the curvature of the lane and vehicle position with respect to center.
  • Warp the detected lane boundaries back onto the original image.
  • Output visual display of the lane boundaries and numerical estimation of lane curvature and vehicle position.

First, I'll compute the camera calibration using chessboard images

In [1]:
import numpy as np
import cv2
import glob
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
%matplotlib inline

def getDistortionCoefs(show_result=False):
    # prepare object points, like (0,0,0), (1,0,0), (2,0,0) ....,(6,5,0)
    objp = np.zeros((6*9,3), np.float32)
    objp[:,:2] = np.mgrid[0:9,0:6].T.reshape(-1,2)

    # Arrays to store object points and image points from all the images.
    objpoints = [] # 3d points in real world space
    imgpoints = [] # 2d points in image plane.

    # Make a list of calibration images
    images = glob.glob('../camera_cal/calibration*.jpg')

    # Step through the list and search for chessboard corners
    for fname in images:
        img = cv2.imread(fname)
        gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)

        # Find the chessboard corners
        ret, corners = cv2.findChessboardCorners(gray, (9,6),None)

        # If found, add object points, image points
        if ret == True:
            objpoints.append(objp)
            imgpoints.append(corners)

            if(show_result):
                # Draw and display the corners
                img = cv2.drawChessboardCorners(img, (9,6), corners, ret)
                plt.imshow(img)
                plt.show()
            
    
    size = cv2.imread(images[0]).shape[0:2]    
    return cv2.calibrateCamera(objpoints, imgpoints, size, None, None)

Calibrate camera

In [49]:
ret, mtx, dist, rvecs, tvecs = getDistortionCoefs(True)

Undistort

In [3]:
#img1 = cv2.imread('../camera_cal/calibration1.jpg')
img1 = cv2.imread('../test_images/test4.jpg')
img2 = cv2.undistort(img1, mtx, dist, None, mtx)

f, (ax1, ax2) = plt.subplots(1, 2, figsize=(20,10))
ax1.imshow(img1)
ax1.set_title('Original Image', fontsize=30)
ax2.imshow(img2)
ax2.set_title('Undistorted Image', fontsize=30)

# cv2.imwrite('calibresult1.png',img1)
# cv2.imwrite('calibresult2.png',img2)
Out[3]:
<matplotlib.text.Text at 0x7febcddf1ba8>

Perspective transform

In [4]:
trans_test = cv2.imread('../test_images/straight_lines1.jpg')
plt.imshow(trans_test)

# plt.plot(200, 710, ".")
# plt.plot(1100, 710, ".")
# plt.plot(560, 470, ".")
# plt.plot(740, 470, ".")


plt.plot(200, 710, ".")
plt.plot(1100, 710, ".")
plt.plot(200, 250, ".")
plt.plot(1100, 250, ".")
Out[4]:
[<matplotlib.lines.Line2D at 0x7febcdd64fd0>]
In [5]:
def warp(img):
    img_size = (img.shape[1],img.shape[0])
    
    src = np.float32([
        [200, 710],
        [1100, 710],
        [560, 470],
        [740, 470]
    ])
    
    dst = np.float32([
        [200, 710],
        [1100, 710],
        [200, 250],
        [1100, 250]
    ])
    
    # Compute the perspective transform, M, given source and destination points:
    M = cv2.getPerspectiveTransform(src, dst)
    
    #Compute the inverse perspective transform:
    Minv = cv2.getPerspectiveTransform(dst, src)

    #Warp an image using the perspective transform, M:
    warped = cv2.warpPerspective(img, M, img_size, flags=cv2.INTER_LINEAR)
    
    return warped, Minv
In [7]:
%matplotlib inline

img1 = mpimg.imread('../test_images/test4.jpg')
img2, Minv = warp(img1)

f, (ax1, ax2) = plt.subplots(1, 2, figsize=(20,10))
ax1.imshow(img1)
ax1.set_title('Original Image', fontsize=30)
ax2.imshow(img2)
ax2.set_title('Warped Image', fontsize=30)
Out[7]:
<matplotlib.text.Text at 0x7febcda70240>

Sobel

In [8]:
# Define a function that takes an image, gradient orientation,
# and threshold min / max values.
def abs_sobel_thresh(img, sobel_kernel=3, orient='x', mag_thresh=(0, 255)):
    # Convert to grayscale
    gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
    # Apply x or y gradient with the OpenCV Sobel() function
    # and take the absolute value
    if orient == 'x':
        abs_sobel = np.absolute(cv2.Sobel(gray, cv2.CV_64F, 1, 0, ksize=sobel_kernel))
    if orient == 'y':
        abs_sobel = np.absolute(cv2.Sobel(gray, cv2.CV_64F, 0, 1, ksize=sobel_kernel))
    # Rescale back to 8 bit integer
    scaled_sobel = np.uint8(255*abs_sobel/np.max(abs_sobel))
    # Create a copy and apply the threshold
    binary_output = np.zeros_like(scaled_sobel)
    # Here I'm using inclusive (>=, <=) thresholds, but exclusive is ok too
    binary_output[(scaled_sobel >= mag_thresh[0]) & (scaled_sobel <= mag_thresh[1])] = 1

    # Return the result
    return binary_output

# Define a function to threshold an image for a given range and Sobel kernel
def dir_threshold(img, sobel_kernel=3, thresh=(0, np.pi/2)):
    # Grayscale
    gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
    # Calculate the x and y gradients
    sobelx = cv2.Sobel(gray, cv2.CV_64F, 1, 0, ksize=sobel_kernel)
    sobely = cv2.Sobel(gray, cv2.CV_64F, 0, 1, ksize=sobel_kernel)
    # Take the absolute value of the gradient direction, 
    # apply a threshold, and create a binary image result
    absgraddir = np.arctan2(np.absolute(sobely), np.absolute(sobelx))
    binary_output =  np.zeros_like(absgraddir)
    binary_output[(absgraddir >= thresh[0]) & (absgraddir <= thresh[1])] = 1

    # Return the binary image
    return binary_output

# Define a function to return the magnitude of the gradient
# for a given sobel kernel size and threshold values
def mag_thresh(img, sobel_kernel=3, mag_thresh=(0, 255)):
    # Convert to grayscale
    gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
    # Take both Sobel x and y gradients
    sobelx = cv2.Sobel(gray, cv2.CV_64F, 1, 0, ksize=sobel_kernel)
    sobely = cv2.Sobel(gray, cv2.CV_64F, 0, 1, ksize=sobel_kernel)
    # Calculate the gradient magnitude
    gradmag = np.sqrt(sobelx**2 + sobely**2)
    # Rescale to 8 bit
    scale_factor = np.max(gradmag)/255 
    gradmag = (gradmag/scale_factor).astype(np.uint8) 
    # Create a binary image of ones where threshold is met, zeros otherwise
    binary_output = np.zeros_like(gradmag)
    binary_output[(gradmag >= mag_thresh[0]) & (gradmag <= mag_thresh[1])] = 1

    # Return the binary image
    return binary_output

def combo(image):
    # Choose a Sobel kernel size
    ksize = 15 # Choose a larger odd number to smooth gradient measurements

    # Apply each of the thresholding functions
    gradx = abs_sobel_thresh(image, sobel_kernel=3, orient='x', mag_thresh=(10, 100))
    grady = abs_sobel_thresh(image, sobel_kernel=3, orient='y', mag_thresh=(10, 100))
    mag_binary = mag_thresh(image, sobel_kernel=9, mag_thresh=(30, 100))
    dir_binary = dir_threshold(image, sobel_kernel=9, thresh=(0.7, 1.3))
    
    combined = np.zeros_like(dir_binary)
    combined[((gradx == 1) & (grady == 1)) | ((mag_binary == 1) & (dir_binary == 1))] = 1
    
    return combined
In [9]:
image = cv2.imread('../test_images/test4.jpg')

# Run the function
grad_binary = combo(image)
# Plot the result
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(24, 9))
f.tight_layout()
ax1.imshow(image)
ax1.set_title('Original Image', fontsize=50)
ax2.imshow(grad_binary, cmap='gray')
ax2.set_title('Thresholded Gradient', fontsize=50)
plt.subplots_adjust(left=0., right=1, top=0.9, bottom=0.)

Color threshold

In [10]:
def colorThreshold(img):
    # Convert to HLS color space and separate the S channel
    # Note: img is the undistorted image
    hls = cv2.cvtColor(img, cv2.COLOR_RGB2HLS)
    s_channel = hls[:,:,2]

    # Grayscale image
    # NOTE: we already saw that standard grayscaling lost color information for the lane lines
    # Explore gradients in other colors spaces / color channels to see what might work better
    gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)

    # Sobel x
    sobelx = cv2.Sobel(gray, cv2.CV_64F, 1, 0) # Take the derivative in x
    abs_sobelx = np.absolute(sobelx) # Absolute x derivative to accentuate lines away from horizontal
    scaled_sobel = np.uint8(255*abs_sobelx/np.max(abs_sobelx))

    # Threshold x gradient
    thresh_min = 20
    thresh_max = 100
    sxbinary = np.zeros_like(scaled_sobel)
    sxbinary[(scaled_sobel >= thresh_min) & (scaled_sobel <= thresh_max)] = 1

    # Threshold color channel
    s_thresh_min = 170
    s_thresh_max = 255
    s_binary = np.zeros_like(s_channel)
    s_binary[(s_channel >= s_thresh_min) & (s_channel <= s_thresh_max)] = 1

    # Stack each channel to view their individual contributions in green and blue respectively
    # This returns a stack of the two binary images, whose components you can see as different colors
    color_binary = np.dstack(( np.zeros_like(sxbinary), sxbinary, s_binary))

    # Combine the two binary thresholds
    combined_binary = np.zeros_like(sxbinary)
    combined_binary[(s_binary == 1) | (sxbinary == 1)] = 1
    
    return combined_binary
In [11]:
image = cv2.imread('../test_images/test4.jpg')

# Run the function
grad_binary = colorThreshold(image)
# Plot the result
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(24, 9))
f.tight_layout()
ax1.imshow(image)
ax1.set_title('Original Image', fontsize=50)
ax2.imshow(grad_binary, cmap='gray')
ax2.set_title('Thresholded Gradient', fontsize=50)
plt.subplots_adjust(left=0., right=1, top=0.9, bottom=0.)

Locate the Lane Lines and Fit a Polynomial

In [45]:
def getLinesFit(binary_warped, show_result=False):
    # Take a histogram of the bottom half of the image
    histogram = np.sum(binary_warped[int(binary_warped.shape[0]/2):,:], axis=0)
    # Create an output image to draw on and  visualize the result
    out_img = np.dstack((binary_warped, binary_warped, binary_warped))*255
    # Find the peak of the left and right halves of the histogram
    # These will be the starting point for the left and right lines
    midpoint = np.int(histogram.shape[0]/2)
    leftx_base = np.argmax(histogram[:midpoint])
    rightx_base = np.argmax(histogram[midpoint:]) + midpoint

    # Choose the number of sliding windows
    nwindows = 9
    # Set height of windows
    window_height = np.int(binary_warped.shape[0]/nwindows)
    # Identify the x and y positions of all nonzero pixels in the image
    nonzero = binary_warped.nonzero()
    nonzeroy = np.array(nonzero[0])
    nonzerox = np.array(nonzero[1])
    # Current positions to be updated for each window
    leftx_current = leftx_base
    rightx_current = rightx_base
    # Set the width of the windows +/- margin
    margin = 100
    # Set minimum number of pixels found to recenter window
    minpix = 50
    # Create empty lists to receive left and right lane pixel indices
    left_lane_inds = []
    right_lane_inds = []

    # Step through the windows one by one
    for window in range(nwindows):
        # Identify window boundaries in x and y (and right and left)
        win_y_low = binary_warped.shape[0] - (window+1)*window_height
        win_y_high = binary_warped.shape[0] - window*window_height
        win_xleft_low = leftx_current - margin
        win_xleft_high = leftx_current + margin
        win_xright_low = rightx_current - margin
        win_xright_high = rightx_current + margin
        # Draw the windows on the visualization image
        cv2.rectangle(out_img,(win_xleft_low,win_y_low),(win_xleft_high,win_y_high),(0,255,0), 2) 
        cv2.rectangle(out_img,(win_xright_low,win_y_low),(win_xright_high,win_y_high),(0,255,0), 2) 
        # Identify the nonzero pixels in x and y within the window
        good_left_inds = ((nonzeroy >= win_y_low) & (nonzeroy < win_y_high) & (nonzerox >= win_xleft_low) & (nonzerox < win_xleft_high)).nonzero()[0]
        good_right_inds = ((nonzeroy >= win_y_low) & (nonzeroy < win_y_high) & (nonzerox >= win_xright_low) & (nonzerox < win_xright_high)).nonzero()[0]
        # Append these indices to the lists
        left_lane_inds.append(good_left_inds)
        right_lane_inds.append(good_right_inds)
        # If you found > minpix pixels, recenter next window on their mean position
        if len(good_left_inds) > minpix:
            leftx_current = np.int(np.mean(nonzerox[good_left_inds]))
        if len(good_right_inds) > minpix:        
            rightx_current = np.int(np.mean(nonzerox[good_right_inds]))

    # Concatenate the arrays of indices
    left_lane_inds = np.concatenate(left_lane_inds)
    right_lane_inds = np.concatenate(right_lane_inds)

    # Extract left and right line pixel positions
    leftx = nonzerox[left_lane_inds]
    lefty = nonzeroy[left_lane_inds] 
    rightx = nonzerox[right_lane_inds]
    righty = nonzeroy[right_lane_inds] 

    # Fit a second order polynomial to each
    left_fit = np.polyfit(lefty, leftx, 2)
    right_fit = np.polyfit(righty, rightx, 2)
    
    if(show_result):
        # VISUALIZE
        # Generate x and y values for plotting
        ploty = np.linspace(0, binary_warped.shape[0]-1, binary_warped.shape[0] )
        left_fitx = left_fit[0]*ploty**2 + left_fit[1]*ploty + left_fit[2]
        right_fitx = right_fit[0]*ploty**2 + right_fit[1]*ploty + right_fit[2]

        # Create an image to draw on and an image to show the selection window
        out_img = np.dstack((binary_warped, binary_warped, binary_warped))*255
        window_img = np.zeros_like(out_img)
        # Color in left and right line pixels
        out_img[nonzeroy[left_lane_inds], nonzerox[left_lane_inds]] = [255, 0, 0]
        out_img[nonzeroy[right_lane_inds], nonzerox[right_lane_inds]] = [0, 0, 255]

        # Generate a polygon to illustrate the search window area
        # And recast the x and y points into usable format for cv2.fillPoly()
        left_line_window1 = np.array([np.transpose(np.vstack([left_fitx-margin, ploty]))])
        left_line_window2 = np.array([np.flipud(np.transpose(np.vstack([left_fitx+margin, ploty])))])
        left_line_pts = np.hstack((left_line_window1, left_line_window2))
        right_line_window1 = np.array([np.transpose(np.vstack([right_fitx-margin, ploty]))])
        right_line_window2 = np.array([np.flipud(np.transpose(np.vstack([right_fitx+margin, ploty])))])
        right_line_pts = np.hstack((right_line_window1, right_line_window2))

        # Draw the lane onto the warped blank image
        cv2.fillPoly(window_img, np.int_([left_line_pts]), (0,255, 0))
        cv2.fillPoly(window_img, np.int_([right_line_pts]), (0,255, 0))
        result = cv2.addWeighted(out_img, 1, window_img, 0.3, 0)
        plt.imshow(result)
        plt.plot(left_fitx, ploty, color='yellow')
        plt.plot(right_fitx, ploty, color='yellow')
        plt.xlim(0, 1280)
        plt.ylim(720, 0)
    
    return (left_fit, right_fit)

def getNextLinesFit(binary_warped, left_fit, right_fit, show_result = False):
    # Assume you now have a new warped binary image 
    # from the next frame of video (also called "binary_warped")
    # It's now much easier to find line pixels!
    nonzero = binary_warped.nonzero()
    nonzeroy = np.array(nonzero[0])
    nonzerox = np.array(nonzero[1])
    margin = 100
    left_lane_inds = ((nonzerox > (left_fit[0]*(nonzeroy**2) + left_fit[1]*nonzeroy + left_fit[2] - margin)) & (nonzerox < (left_fit[0]*(nonzeroy**2) + left_fit[1]*nonzeroy + left_fit[2] + margin))) 
    right_lane_inds = ((nonzerox > (right_fit[0]*(nonzeroy**2) + right_fit[1]*nonzeroy + right_fit[2] - margin)) & (nonzerox < (right_fit[0]*(nonzeroy**2) + right_fit[1]*nonzeroy + right_fit[2] + margin)))  

    # Again, extract left and right line pixel positions
    leftx = nonzerox[left_lane_inds]
    lefty = nonzeroy[left_lane_inds] 
    rightx = nonzerox[right_lane_inds]
    righty = nonzeroy[right_lane_inds]
    # Fit a second order polynomial to each
    left_fit = np.polyfit(lefty, leftx, 2)
    right_fit = np.polyfit(righty, rightx, 2)
    
    
    if(show_result):
        # VISUALIZE
        # Generate x and y values for plotting
        ploty = np.linspace(0, binary_warped.shape[0]-1, binary_warped.shape[0] )
        left_fitx = left_fit[0]*ploty**2 + left_fit[1]*ploty + left_fit[2]
        right_fitx = right_fit[0]*ploty**2 + right_fit[1]*ploty + right_fit[2]

        # Create an image to draw on and an image to show the selection window
        out_img = np.dstack((binary_warped, binary_warped, binary_warped))*255
        window_img = np.zeros_like(out_img)
        # Color in left and right line pixels
        out_img[nonzeroy[left_lane_inds], nonzerox[left_lane_inds]] = [255, 0, 0]
        out_img[nonzeroy[right_lane_inds], nonzerox[right_lane_inds]] = [0, 0, 255]

        # Generate a polygon to illustrate the search window area
        # And recast the x and y points into usable format for cv2.fillPoly()
        left_line_window1 = np.array([np.transpose(np.vstack([left_fitx-margin, ploty]))])
        left_line_window2 = np.array([np.flipud(np.transpose(np.vstack([left_fitx+margin, ploty])))])
        left_line_pts = np.hstack((left_line_window1, left_line_window2))
        right_line_window1 = np.array([np.transpose(np.vstack([right_fitx-margin, ploty]))])
        right_line_window2 = np.array([np.flipud(np.transpose(np.vstack([right_fitx+margin, ploty])))])
        right_line_pts = np.hstack((right_line_window1, right_line_window2))

        # Draw the lane onto the warped blank image
        cv2.fillPoly(window_img, np.int_([left_line_pts]), (0,255, 0))
        cv2.fillPoly(window_img, np.int_([right_line_pts]), (0,255, 0))
        result = cv2.addWeighted(out_img, 1, window_img, 0.3, 0)
        plt.imshow(result)
        plt.plot(left_fitx, ploty, color='yellow')
        plt.plot(right_fitx, ploty, color='yellow')
        plt.xlim(0, 1280)
        plt.ylim(720, 0)
    
    return (left_fit, right_fit)

Measuring Curvature

In [13]:
def getCurvature(left_fit, right_fit):
    # Generate some fake data to represent lane-line pixels
    ploty = np.linspace(0, 719, num=720)# to cover same y-range as image
    quadratic_coeff = 3e-4 # arbitrary quadratic coefficient
    # For each y position generate random x position within +/-50 pix
    # of the line base position in each case (x=200 for left, and x=900 for right)
    leftx = np.array([200 + (y**2)*quadratic_coeff + np.random.randint(-50, high=51) 
                                  for y in ploty])
    rightx = np.array([900 + (y**2)*quadratic_coeff + np.random.randint(-50, high=51) 
                                    for y in ploty])

    leftx = leftx[::-1]  # Reverse to match top-to-bottom in y
    rightx = rightx[::-1]  # Reverse to match top-to-bottom in y


    # Fit a second order polynomial to pixel positions in each fake lane line
    left_fit = np.polyfit(ploty, leftx, 2)
    left_fitx = left_fit[0]*ploty**2 + left_fit[1]*ploty + left_fit[2]
    right_fit = np.polyfit(ploty, rightx, 2)
    right_fitx = right_fit[0]*ploty**2 + right_fit[1]*ploty + right_fit[2]

    # Plot up the fake data
    #     mark_size = 3
    #     plt.plot(leftx, ploty, 'o', color='red', markersize=mark_size)
    #     plt.plot(rightx, ploty, 'o', color='blue', markersize=mark_size)
    #     plt.xlim(0, 1280)
    #     plt.ylim(0, 720)
    #     plt.plot(left_fitx, ploty, color='green', linewidth=3)
    #     plt.plot(right_fitx, ploty, color='green', linewidth=3)
    #     plt.gca().invert_yaxis() # to visualize as we do the images
    
    # Define y-value where we want radius of curvature
    # I'll choose the maximum y-value, corresponding to the bottom of the image
    y_eval = np.max(ploty)
    left_curverad = ((1 + (2*left_fit[0]*y_eval + left_fit[1])**2)**1.5) / np.absolute(2*left_fit[0])
    right_curverad = ((1 + (2*right_fit[0]*y_eval + right_fit[1])**2)**1.5) / np.absolute(2*right_fit[0])
    #print(left_curverad, right_curverad)
    
    # Define conversions in x and y from pixels space to meters
    ym_per_pix = 30/720 # meters per pixel in y dimension
    xm_per_pix = 3.7/700 # meters per pixel in x dimension

    # Fit new polynomials to x,y in world space
    left_fit_cr = np.polyfit(ploty*ym_per_pix, leftx*xm_per_pix, 2)
    right_fit_cr = np.polyfit(ploty*ym_per_pix, rightx*xm_per_pix, 2)
    # Calculate the new radii of curvature
    left_curverad = ((1 + (2*left_fit_cr[0]*y_eval*ym_per_pix + left_fit_cr[1])**2)**1.5) / np.absolute(2*left_fit_cr[0])
    right_curverad = ((1 + (2*right_fit_cr[0]*y_eval*ym_per_pix + right_fit_cr[1])**2)**1.5) / np.absolute(2*right_fit_cr[0])
    # Now our radius of curvature is in meters
    return (left_curverad, right_curverad)

Find car position

In [14]:
def findCarPosition(img, curve):
    shape = img.shape[0:2]
    left_line_y = np.polyval(curve[0], shape[0])
    right_line_y = np.polyval(curve[1], shape[0])
    lane_center = left_line_y + (right_line_y - left_line_y) / 2
    car_to_lane = shape[1] / 2 - lane_center
    car_to_lane_m = round(abs(car_to_lane * 30/720),2) # in meters
    
    position = "center"
    if(car_to_lane < 0):
        position = "left"
    elif (car_to_lane > 0):
        position = "right"
    #print(position, car_to_lane, car_to_lane_m,"m")    
    #print(left_line_y, right_line_y, lane_center)
    
    return car_to_lane_m, position

Pipeline

In [15]:
def drawResults(undist, warped, Minv, left_fit, right_fit):
    # Generate x and y values for plotting
    ploty = np.linspace(0, warped.shape[0]-1, warped.shape[0] )
    left_fitx = left_fit[0]*ploty**2 + left_fit[1]*ploty + left_fit[2]
    right_fitx = right_fit[0]*ploty**2 + right_fit[1]*ploty + right_fit[2]
    
    # Create an image to draw the lines on
    warp_zero = np.zeros_like(warped).astype(np.uint8)
    color_warp = np.dstack((warp_zero, warp_zero, warp_zero))

    # Recast the x and y points into usable format for cv2.fillPoly()
    pts_left = np.array([np.transpose(np.vstack([left_fitx, ploty]))])
    pts_right = np.array([np.flipud(np.transpose(np.vstack([right_fitx, ploty])))])
    pts = np.hstack((pts_left, pts_right))

    # Draw the lane onto the warped blank image
    cv2.fillPoly(color_warp, np.int_([pts]), (0,255, 0))
    
    # Warp the blank back to original image space using inverse perspective matrix (Minv)
    newwarp = cv2.warpPerspective(color_warp, Minv, (warped.shape[1], warped.shape[0])) 
    # Combine the result with the original image
    result = cv2.addWeighted(undist, 1, newwarp, 0.3, 0)
    
    return result
In [46]:
def pipeline(img1, curve=None):
    show_result = True
    
    # 1. camera calibration matrix and distortion coefficients
    #ret, mtx, dist, rvecs, tvecs = getDistortionCoefs(show_result)

    # 2. apply them to undistort each new frame
    img2 = cv2.undistort(img1, mtx, dist, None, mtx)

    # 3. apply thresholds to create a binary image
    img2 = colorThreshold(img2)

    # 4. apply a perspective transform.
    img2, Minv = warp(img2)
    
    if(show_result):
        # Plot the result
        f, (ax1, ax2) = plt.subplots(1, 2, figsize=(24, 9))
        f.tight_layout()
        ax1.imshow(img1)
        ax1.set_title('Original Image', fontsize=50)
        ax2.imshow(img2, cmap='gray')
        ax2.set_title('Result', fontsize=50)
        plt.subplots_adjust(left=0., right=1, top=0.9, bottom=0.)
        plt.show()
        
    # 5. Locate the Lane Lines and Fit a Polynomial
    if(curve is None):
        # get starting curve
        curve = getLinesFit(img2, show_result)
    else:  
        # continue with existing curve
        curve = getNextLinesFit(img2, curve[0], curve[1], show_result) 
    
    if(show_result):
        plt.show()
    
    # 6. Find vehicle position
    position_m, position_side = findCarPosition(img1, curve)
    if(show_result):
        print(position_m, 'm', position_side)
    
    # 7. Measuring Curvature
    curvature = getCurvature(curve[0], curve[1]) 
    if(show_result):
        print(curvature[0], 'm', curvature[1], 'm')
    
    # 8. Show results
    result = drawResults(img1, img2, Minv, curve[0], curve[1])
    
    return result, curve
    
In [48]:
images = glob.glob('../test_images/test*.jpg')
images.append('../test_images/straight_lines1.jpg')
images.append('../test_images/straight_lines2.jpg')
for image in images:
    result, curve = pipeline(img1 = cv2.imread(image))
    plt.imshow(result)
    plt.show()
    
    
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Process video

In [37]:
# Import everything needed to edit/save/watch video clips
from moviepy.editor import VideoFileClip
from IPython.display import HTML

def process_image(image):
    result, curve = pipeline(image)
    return result
In [41]:
white_output = '../test_videos/project_video_result.mp4'
clip1 = VideoFileClip("../project_video.mp4")
white_clip = clip1.fl_image(process_image) #NOTE: this function expects color images!!
%time white_clip.write_videofile(white_output, audio=False)
[MoviePy] >>>> Building video ../test_videos/project_video_result.mp4
[MoviePy] Writing video ../test_videos/project_video_result.mp4
100%|█████████▉| 1260/1261 [03:50<00:00,  5.79it/s]
[MoviePy] Done.
[MoviePy] >>>> Video ready: ../test_videos/project_video_result.mp4 

CPU times: user 3min 49s, sys: 5.29 s, total: 3min 55s
Wall time: 3min 52s
In [39]:
white_output = '../test_videos/challenge_video_result.mp4'
clip1 = VideoFileClip("../challenge_video.mp4")
white_clip = clip1.fl_image(process_image) #NOTE: this function expects color images!!
%time white_clip.write_videofile(white_output, audio=False)
[MoviePy] >>>> Building video ../test_videos/challenge_video_result.mp4
[MoviePy] Writing video ../test_videos/challenge_video_result.mp4
100%|██████████| 485/485 [01:23<00:00,  6.76it/s]
[MoviePy] Done.
[MoviePy] >>>> Video ready: ../test_videos/challenge_video_result.mp4 

CPU times: user 1min 23s, sys: 2.42 s, total: 1min 25s
Wall time: 1min 25s
In [40]:
white_output = '../test_videos/harder_challenge_video_result.mp4'
clip1 = VideoFileClip("../harder_challenge_video.mp4")
white_clip = clip1.fl_image(process_image) #NOTE: this function expects color images!!
%time white_clip.write_videofile(white_output, audio=False)
[MoviePy] >>>> Building video ../test_videos/harder_challenge_video_result.mp4
[MoviePy] Writing video ../test_videos/harder_challenge_video_result.mp4
100%|█████████▉| 1199/1200 [03:35<00:00,  4.36it/s]
[MoviePy] Done.
[MoviePy] >>>> Video ready: ../test_videos/harder_challenge_video_result.mp4 

CPU times: user 3min 33s, sys: 3.98 s, total: 3min 37s
Wall time: 3min 39s
In [42]:
white_output = '../test_videos/project_video_result.mp4'

HTML("""
<video width="960" height="540" controls>
  <source src="{0}">
</video>
""".format(white_output))
Out[42]:
In [ ]: